Description

This dataset contains daily historical weather data recorded at multiple weather stations from January 1, 2020, to December 30, 2020. The data includes temperature, precipitation, humidity, wind speed, and weather conditions, providing a comprehensive view of the weather patterns over the year. This dataset is ideal for climate analysis, weather prediction, and educational purposes.

Columns

• Date: The date of the observation.
• Station: The weather station identifier.
• Temperature: The recorded temperature (in Celsius).
• Precipitation: The recorded precipitation (in mm).
• Humidity: The recorded humidity (in %).
• WindSpeed: The recorded wind speed (in km/h).
• WeatherCondition: The recorded weather condition (e.g., sunny, rainy, snowy).

Source

Data generated synthetically for educational purposes.

Potential Uses

• Climate change analysis
• Weather pattern prediction
• Agricultural planning

OnPoint Weather Historical data provides hourly and daily weather values from the year 2000 to present. This database is a stable source of historical information from 2007 for North American and back to 2000 for all other international locations because once the data is archived no further changes or edits are made.

Dataset from National Environment Agency. For more information, visit https://data.gov.sg/datasets/d_03bb2eb67ad645d0188342fa74ad7066/view

The backbone of CustomWeather's forecasting arm is our proven, high-resolution model, the CW100. The CW100 Model is based on physics, not statistics or airport observations. As a result, it can achieve significantly better accuracy than statistical models, especially for non-airport locations. While other forecast models are designed to forecast the entire atmosphere, the CW100 greatly reduces computational requirements by focusing entirely on conditions near the ground. This reduction of computations allows it to resolve additional physical processes near the ground that are not resolved by other models. It also allows the CW100 to operate at a much higher resolution, typically 100x finer than standard models and other gridded forecasts.

Detailed Forecasts:
Features a detailed 48-hour outlook broken into four segments per day: morning, afternoon, evening, and overnight. Each segment provides condition descriptions, high/low temperatures, wind speed and direction, humidity, comfort level, UV index, expected and probability of precipitation, 6-hr forecasted precip amounts, and miles of visibility. Available for over 85,000 forecast points globally. The information is updated four times per day.

Extended Forecasts Days 1-15:
Features condition descriptions, high/low temperatures, wind speed and direction, humidity, comfort level, UV index, expected and probability of precipitation, and miles of visibility. Available for over 85,000 forecast points globally. The information is updated four times per day.

Hour-by-Hour Forecasts: Features Hour-by-Hour forecasts. The product is available as 12 hour, 48 hour and 168 hour blocks. Each hourly forecast includes weather descriptions, wind conditions, temperature, dew point, humidity, visibility, rainfall totals, snowfall totals, and precipitation probability. Available for over 85,000 forecast points globally. Updated four times per day.

Historical Longer Term Forecasts: Includes historical hourly and/or daily forecast data from 2009 until present date. Data will include condition descriptions, high/low temperatures, wind speed and direction, dew point, humidity, comfort level, UV index, probability of precipitation, rainfall and snowfall amounts. Available for over 85,000 forecast points globally. The information is updated four times per day.

Below are available time periods per each type of forecast from the GFS model and from CustomWeather's proprietary CW100 model:

GFS: 7-day hourly forecasts from August 2nd 2009; 48-hour to 5-day detailed forecasts from August 4th 2009; 15-day forecasts from October 9th 2008.

CW100: 7-day hourly forecasts from November 27, 2012; 48-hour detailed forecasts from November 12, 2011; 7-day forecasts from December 6, 2010, 15-day forecasts from August 6, 2012. CW100 is CustomWeather's proprietary model.

MOS: (Model Output Statistics) for any global location using archive of model and observation data. 0.25 degree resolution. 15-day hourly forecasts from January 1, 2017; 15-day forecasts from April 19, 2017.

This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Historical. The data include parameters of historical with a geographic location of Switzerland, Western Europe. The time period coverage is from 425 to -39 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

Image Cover

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1842206%2F057ecc16d2aae27eb32391e59599415e%2Fjp2.jpg?generation=1730030359772976&alt=media" alt="">

Context

Hourly and Daily Weather Dataset of Forbes Top 100 Best Cities To Live, Work And Visit from https://open-meteo.com/ from January 01, 2020 to Apr 06, 2025.

Field documentation go here

• https://open-meteo.com/en/docs/historical-weather-api#start_date=2020-01-01&end_date=2025-04-06
• Datetime is in GMT

Visually verify coordinates

• Replace the placeholders with the latitude and longitude values of the cities https://www.latlong.net/c/?lat=(latitude)&long=(longitude)
• Another way to visualize the coordinates https://www.ncei.noaa.gov/maps/radar/

Citations and Acknowledgements

• Zippenfenig, P. (2023). Open-Meteo.com Weather API [Computer software]. Zenodo. https://doi.org/10.5281/ZENODO.7970649
• Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. (2023). ERA5 hourly data on single levels from 1940 to present [Data set]. ECMWF. https://doi.org/10.24381/cds.adbb2d47
• Muñoz Sabater, J. (2019). ERA5-Land hourly data from 2001 to present [Data set]. ECMWF. https://doi.org/10.24381/CDS.E2161BAC
• Schimanke S., Ridal M., Le Moigne P., Berggren L., Undén P., Randriamampianina R., Andrea U., Bazile E., Bertelsen A., Brousseau P., Dahlgren P., Edvinsson L., El Said A., Glinton M., Hopsch S., Isaksson L., Mladek R., Olsson E., Verrelle A., Wang Z.Q. (2021). CERRA sub-daily regional reanalysis data for Europe on single levels from 1984 to present [Data set]. ECMWF. https://doi.org/10.24381/CDS.622A565A

Forbes Top 100 Cities Weather Datasets

• 🌍Forbes Top 100 Cities Weather Data🏙️(1980-1989)
• 🌍Forbes Top 100 Cities Weather Data🏙️(1990-1999)
• 🌍Forbes Top 100 Cities Weather Data🏙️(2000-2009)
• 🌍Forbes Top 100 Cities Weather Data🏙️(2010-2019)
• 🌍Forbes Top 100 Cities Weather Data🏙️(2020-YTD)

Note

Image generated with Bing Image Generator

OnPoint Weather is a global weather dataset for business available for any lat/lon point and geographic area such as ZIP codes. OnPoint Weather provides a continuum of hourly and daily weather from the year 2000 to current time and a forward forecast of 45 days. OnPoint Climatology provides hourly and daily weather statistics which can be used to determine ‘departures from normal’ and to provide climatological guidance of expected weather for any location at any point in time. The OnPoint Climatology provides weather statistics such as means, standard deviations and frequency of occurrence. Weather has a significant impact on businesses and accounts for hundreds of billions in lost revenue annually. OnPoint Weather allows businesses to quantify weather impacts and develop strategies to optimize for weather to improve business performance. Examples of Usage Quantify the impact of weather on sales across diverse locations and times of the year Understand how supply chains are impacted by weather Understand how employee’s attendance and performance are impacted by weather Understand how weather influences foot traffic at malls, stores and restaurants OnPoint Weather is available through Google Cloud Platform’s Commercial Dataset Program and can be easily integrated with other Google Cloud Platform Services to quickly reveal and quantify weather impacts on business. Weather Source provides a full range of support services from answering quick questions to consulting and building custom solutions. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery 瞭解詳情

This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Historical. The data include parameters of historical with a geographic location of . The time period coverage is from Unavailable begin date to Unavailable end date in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

Note that 2013 and 2014 datasets are available for download in the attachment tab below.The journal article describing GHCN-Daily is: Menne, M.J., I. Durre, R.S. Vose, B.E. Gleason, and T.G. Houston, 2012: An overview of the Global Historical Climatology Network-Daily Database. Journal of Atmospheric and Oceanic Technology, 29, 897-910, doi:10.1175/JTECH-D-11-00103.1.Menne, M.J., I. Durre, B. Korzeniewski, S. McNeal, K. Thomas, X. Yin, S. Anthony, R. Ray, R.S. Vose, B.E.Gleason, and T.G. Houston, 2012: Global Historical Climatology Network - Daily (GHCN-Daily), Version 3. [indicate subset used following decimal, e.g. Version 3.12]. NOAA National Climatic Data Center. http://doi.org/10.7289/V5D21VHZ

The backbone of CustomWeather's forecasting arm is our proven, high-resolution model, the CW100. The CW100 Model is based on physics, not statistics or airport observations. As a result, it can achieve significantly better accuracy than statistical models, especially for non-airport locations. While other forecast models are designed to forecast the entire atmosphere, the CW100 greatly reduces computational requirements by focusing entirely on conditions near the ground. This reduction of computations allows it to resolve additional physical processes near the ground that are not resolved by other models. It also allows the CW100 to operate at a much higher resolution, typically 100x finer than standard models and other gridded forecasts.

Detailed Forecasts:
Features a detailed 48-hour outlook broken into four segments per day: morning, afternoon, evening, and overnight. Each segment provides condition descriptions, high/low temperatures, wind speed and direction, humidity, comfort level, UV index, expected and probability of precipitation, 6-hr forecasted precip amounts, and miles of visibility. Available for over 85,000 forecast points globally. The information is updated four times per day.

Extended Forecasts Days 1-15:
Features condition descriptions, high/low temperatures, wind speed and direction, humidity, comfort level, UV index, expected and probability of precipitation, and miles of visibility. Available for over 85,000 forecast points globally. The information is updated four times per day.

Hour-by-Hour Forecasts: Features Hour-by-Hour forecasts. The product is available as 12 hour, 48 hour and 168 hour blocks. Each hourly forecast includes weather descriptions, wind conditions, temperature, dew point, humidity, visibility, rainfall totals, snowfall totals, and precipitation probability. Available for over 85,000 forecast points globally. Updated four times per day.

Historical Longer Term Forecasts: Includes historical hourly and/or daily forecast data from 2009 until present date. Data will include condition descriptions, high/low temperatures, wind speed and direction, dew point, humidity, comfort level, UV index, probability of precipitation, rainfall and snowfall amounts. Available for over 85,000 forecast points globally. The information is updated four times per day.

Below are available time periods per each type of forecast from the GFS model and from CustomWeather's proprietary CW100 model:

GFS: 7-day hourly forecasts from August 2nd 2009; 48-hour to 5-day detailed forecasts from August 4th 2009; 15-day forecasts from October 9th 2008.

CW100: 7-day hourly forecasts from November 27, 2012; 48-hour detailed forecasts from November 12, 2011; 7-day forecasts from December 6, 2010, 15-day forecasts from August 6, 2012. CW100 is CustomWeather's proprietary model.

MOS: (Model Output Statistics) for any global location using archive of model and observation data. 0.25 degree resolution. 15-day hourly forecasts from January 1, 2017; 15-day forecasts from April 19, 2017.

Context

The data set was collected by the KNMI (Dutch weather institute) and contains information on weather collected from 50 weather stations across The Netherlands.

Content

This data consists of two datasets: a) KNMI_stations_2018.csv and b) KNMI_20181231.csv.

The data set KNMI_stations_2018.csv contains general information on the weather stations. That is longitude (LON) and latitude (LAT), altitude (in meters) and the general name of that weather station.

The data set KNMI_20181231 contains information about the different weather variables measured by each weather station i.e. temperature, wind speed, precipitation etc.

The data was collected from 1901 to 2018.

Acknowledgements

Source: KONINKLIJK NEDERLANDS METEOROLOGISCH INSTITUUT (KNMI)

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

Weather Source, a leading provider of weather and climate technologies for business intelligence, is offering complimentary data for those researching the potential connections between weather and COVID-19 viability and transmission. This share includes: Global historical weather data dating back to October 2019 Present data Forecast data out to 15 days The data supports temperature and humidity, both specific and relative, at the daily level. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery . This dataset is created and owned by Weather Source and made available for educational and academic research purposes. This dataset has significant public interest in light of the COVID-19 crisis. All bytes processed in queries against this dataset will be zeroed out, making this part of the query free. Data joined with the dataset will be billed at the normal rate to prevent abuse. After September 15, queries over these datasets will revert to the normal billing rate.

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.

Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the contiguous United States are ensemble mean values across 20 global climate models from the CMIP5 experiment (https://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1), downscaled to a 4 km grid. For more information on the downscaling method and to access the data, please see Abatzoglou and Brown, 2012 (https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.2312) and the Northwest Knowledge Network (https://climate.northwestknowledge.net/MACA/). We used the MACAv2- Metdata monthly dataset; average temperature values were calculated as the mean of monthly minimum and maximum air temperature values (degrees C), averaged over the season of interest (annual, winter, or summer). Absolute and percent change were then calculated between the historical and future time periods.

Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the state of Alaska were developed by the Scenarios Network for Alaska and Arctic Planning (SNAP) (https://snap.uaf.edu). These datasets have several important differences from the MACAv2-Metdata (https://climate.northwestknowledge.net/MACA/) products, used in the contiguous U.S. They were developed using different global circulation models and different downscaling methods, and were downscaled to a different scale (771 m instead of 4 km). While these cover the same time periods and use broadly similar approaches, caution should be used when directly comparing values between Alaska and the contiguous United States.

Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

Context

The dataset featured below was created by reconciling measurements from requests of individual weather attributes provided by the European Climate Assessment (ECA). The measurements of this particular dataset were recorded by a weather station near Heathrow airport in London, UK.

-> This weather dataset is a great addition to this London Energy Dataset. You can join both datasets on the 'date' attribute, after some preprocessing, and perform some interesting data analytics regarding how energy consumption was impacted by the weather in London.

Content

The size for the file featured within this Kaggle dataset is shown below — along with a list of attributes and their description summaries: - london_weather.csv - 15341 observations x 10 attributes

1. date - recorded date of measurement - (int)
2. cloud_cover - cloud cover measurement in oktas - (float)
3. sunshine - sunshine measurement in hours (hrs) - (float)
4. global_radiation - irradiance measurement in Watt per square meter (W/m2) - (float)
5. max_temp - maximum temperature recorded in degrees Celsius (°C) - (float)
6. mean_temp - mean temperature in degrees Celsius (°C) - (float)
7. min_temp - minimum temperature recorded in degrees Celsius (°C) - (float)
8. precipitation - precipitation measurement in millimeters (mm) - (float)
9. pressure - pressure measurement in Pascals (Pa) - (float)
10. snow_depth - snow depth measurement in centimeters (cm) - (float)

Source

Weather Data - https://www.ecad.eu/dailydata/index.php

ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. Data is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. ERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities. An uncertainty estimate is sampled by an underlying 10-member ensemble at three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience. Such uncertainty estimates are closely related to the information content of the available observing system which has evolved considerably over time. They also indicate flow-dependent sensitive areas. To facilitate many climate applications, monthly-mean averages have been pre-calculated too, though monthly means are not available for the ensemble mean and spread. ERA5 is updated daily with a latency of about 5 days. In case that serious flaws are detected in this early release (called ERA5T), this data could be different from the final release 2 to 3 months later. In case that this occurs users are notified. The data set presented here is a regridded subset of the full ERA5 data set on native resolution. It is online on spinning disk, which should ensure fast and easy access. It should satisfy the requirements for most common applications. An overview of all ERA5 datasets can be found in this article. Information on access to ERA5 data on native resolution is provided in these guidelines. Data has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for the uncertainty estimate (0.5 and 1 degree respectively for ocean waves). There are four main sub sets: hourly and monthly products, both on pressure levels (upper air fields) and single levels (atmospheric, ocean-wave and land surface quantities). The present entry is "ERA5 hourly data on single levels from 1940 to present".

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

Future Farm, weather, Research, Teaching, Global Farm Platform, Regression, ANOVA, multivariate, THI, heat stress Harper Adams University's Future Farm has compiled historical weather and Temperature Humidity Index (THI) data from 2000 to the present, using the NASA Power daily weather database centred on the farm’s location. This dataset provides comprehensive records of key meteorological variables, including temperature, humidity, and solar radiation, enabling analysis of long-term climatic trends and THI variations. The data supports research into environmental impacts on livestock and crop production, facilitating studies on heat stress in dairy cattle and the development of adaptive management strategies within the university’s agritech initiatives. 1. T2M: This represents the 2-meter air temperature, which is the daily average temperature measured at a height of 2 meters above ground level (°C). 2. PRECTOTCORR: This is the corrected total precipitation, representing the daily total amount of precipitation (rainfall) recorded in millimeters (mm), corrected for various biases. 3. ALLSKY_SFC_PAR_TOT: This is the total surface photosynthetically active radiation (PAR) under all-sky conditions. It measures the amount of light available for photosynthesis (400-700 nm wavelength) reaching the surface in megajoules per square meter (MJ/m²). 4. GWETTOP: This stands for the top layer soil moisture. It measures the moisture content in the top few centimeters of the soil surface, which is crucial for understanding soil and crop conditions. 5. GWETROOT: This indicates the root zone soil moisture, which measures the moisture level within the rooting zone of the soil profile. It is essential for assessing water availability for crops and vegetation. 6. CLOUD_AMT: This represents the total cloud amount or cloud cover, indicating the fraction of the sky covered by clouds, expressed as a percentage (%). 7. RH2M: This is the relative humidity at 2 meters above ground level, measuring the daily average humidity at this height. It is expressed as a percentage (%), indicating the amount of moisture in the air relative to the maximum moisture the air can hold at that temperature. 8. THI (Temperature-Humidity Index): THI is a calculated index that combines air temperature (T2M) and relative humidity (RH2M) to estimate heat stress in animals, particularly livestock like dairy cows. It is expressed as a dimensionless value and is used to assess the impact of environmental conditions on animal well-being, productivity, and comfort levels. High THI values indicate higher heat stress potential.

The average temperature in the contiguous United States reached 55.5 degrees Fahrenheit (13 degrees Celsius) in 2024, approximately 3.5 degrees Fahrenheit higher than the 20th-century average. These levels represented a record since measurements started in ****. Monthly average temperatures in the U.S. were also indicative of this trend. Temperatures and emissions are on the rise The rise in temperatures since 1975 is similar to the increase in carbon dioxide emissions in the U.S. Although CO₂ emissions in recent years were lower than when they peaked in 2007, they were still generally higher than levels recorded before 1990. Carbon dioxide is a greenhouse gas and is the main driver of climate change. Extreme weather Scientists worldwide have found links between the rise in temperatures and changing weather patterns. Extreme weather in the U.S. has resulted in natural disasters such as hurricanes and extreme heat waves becoming more likely. Economic damage caused by extreme temperatures in the U.S. has amounted to hundreds of billions of U.S. dollars over the past few decades.

Records of past climate and environment from historical references and documentary evidence such as church records, harvest dates, and diaries. Parameter keywords describe what was measured in this data set. Additional summary information can be found in the abstracts of papers listed in the data set citations. For details please see: http://www.ncdc.noaa.gov/paleo/historical.html

Q: What are the chances for various temperature conditions over the next three months? A: Shaded areas show where average temperature has an increased chance of being warmer or cooler than usual. The darker the shading, the greater the chance for the indicated condition. White areas have equal chances for average temperatures below, near, or above the long-term average for the month. Q: What data do experts use to develop these forecasts? A: Climate scientists base future climate outlooks on current patterns in the ocean and atmosphere. They examine projections from climate and weather models and consider recent trends. They also check historical records to see what temperature conditions resulted from similar patterns in the past. Q: What do the colors mean? A: Colors on the map show experts’ level of confidence in their forecasts for above- or below-average temperatures. Each location on the map has some chance to experience average temperatures that rank in the bottom, middle, or top of records from the previous three decades. White areas have equal chances for all three conditions. Colors show where the odds for one of the conditions are higher than for the other two. A common mistake is to interpret these maps as predicted temperatures. However, dark orange areas are not predicted to be warmer than light orange areas. The dark orange areas simply have a higher likelihood for above-average temperatures than the light orange areas do. Similarly, dark blue areas are not predicted to be cooler than light blue areas. Keep in mind that outlooks show the most likely condition for each region, not the only possible outcome. Q: Why do these data matter? A: Energy companies want to know how much energy people will need in the next three months. Temperature outlooks can inform them when they should prepare to meet high demand for energy. Outlooks can also help them choose the best time to schedule maintenance procedures. Forestry managers also check temperature outlooks for the upcoming season. When they see increased chances for warmer-than-usual weather, they may take extra measures to prepare for more wildfires. Managers in agricultural industries also want to know if temperatures are likely to be warmer or cooler than usual. This information can help them optimize food production. Q: How did you produce these snapshots? A: Data Snapshots are derivatives of existing data products: to meet the needs of a broad audience, we present the source data in a simplified visual style. NOAA's Climate Prediction Center (CPC) produces the source images for monthly temperature outlooks. To produce our images, we run a set of scripts that access mapping layers from CPC, re-project them into desired projections at various sizes, and output them with a custom color bar. References One-Month to Three-Month Climate Outlooks. http://www.cpc.ncep.noaa.gov/products/forecasts/ Source: https://www.climate.gov/maps-data/data-snapshots/data-source/temperature-three-month-outlookThis upload includes two additional files:* Temperature - Three-Month Outlook _NOAA Climate.gov.pdf is a screenshot of the main Climate.gov site for these snapshots (https://www.climate.gov/maps-data/data-snapshots/data-source/temperature-three-month-outlook)* Cimate_gov_ Data Snapshots.pdf is a screenshot of the data download page for the full-resolution files.